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Functional data analysis: An introduction and recent developments
Functional data analysis (FDA) is a statistical framework that allows for the analysis of
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …
[ספר][B] Advanced methods for fault diagnosis and fault-tolerant control
SX Ding - 2021 - Springer
This book is the third one in my book series plan. While the first two are dedicated to model-
based and data-driven fault diagnosis respectively, this one addresses topics in both model …
based and data-driven fault diagnosis respectively, this one addresses topics in both model …
[ספר][B] Statistical shape analysis: with applications in R
A thoroughly revised and updated edition of this introduction to modern statistical methods
for shape analysis Shape analysis is an important tool in the many disciplines where objects …
for shape analysis Shape analysis is an important tool in the many disciplines where objects …
Shape-based functional data analysis
Functional data analysis (FDA) is a fast-growing area of research and development in
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …
A Grassmann manifold handbook: Basic geometry and computational aspects
The Grassmann manifold of linear subspaces is important for the mathematical modelling of
a multitude of applications, ranging from problems in machine learning, computer vision and …
a multitude of applications, ranging from problems in machine learning, computer vision and …
A hyperbolic-to-hyperbolic graph convolutional network
Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation
ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to …
ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to …
[ספר][B] Shapes and diffeomorphisms
L Younes - 2010 - Springer
Implicit representations can provide simple descriptions of relatively complex shapes and
can in many cases be a good choice when designing stable shape processing algorithms …
can in many cases be a good choice when designing stable shape processing algorithms …
Fréchet regression for random objects with Euclidean predictors
A Petersen, HG Müller - 2019 - projecteuclid.org
Frechet regression for random objects with Euclidean predictors Page 1 The Annals of Statistics
2019, Vol. 47, No. 2, 691–719 https://doi.org/10.1214/17-AOS1624 © Institute of Mathematical …
2019, Vol. 47, No. 2, 691–719 https://doi.org/10.1214/17-AOS1624 © Institute of Mathematical …
Geodesic exponential kernels: When curvature and linearity conflict
We consider kernel methods on general geodesic metric spaces and provide both negative
and positive results. First we show that the common Gaussian kernel can only be …
and positive results. First we show that the common Gaussian kernel can only be …
Non-euclidean universal approximation
Modifications to a neural network's input and output layers are often required to
accommodate the specificities of most practical learning tasks. However, the impact of such …
accommodate the specificities of most practical learning tasks. However, the impact of such …